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Metabologenomics and Network Pharmacology to Understand the Molecular Mechanism of Cancer Research

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Specialty General Medicine
Date 2024 Feb 7
PMID 38322468
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Abstract

In this editorial I comment on the article "Network pharmacological and molecular docking study of the effect of Liu-Wei-Bu-Qi capsule on lung cancer" published in the recent issue of the 2023 November 6; 11 (31): 7593-7609. Almost all living forms are able to manufacture particular chemicals-metabolites that enable them to differentiate themselves from one another and to overcome the unique obstacles they encounter in their natural habitats. Numerous methods for chemical warfare, communication, nutrition acquisition, and stress prevention are made possible by these specialized metabolites. Metabolomics is a popular technique for collecting direct measurements of metabolic activity from many biological systems. However, confusing metabolite identification is a typical issue, and biochemical interpretation is frequently constrained by imprecise and erroneous genome-based estimates of enzyme activity. Metabolite annotation and gene integration uses a biochemical reaction network to obtain a metabolite-gene association so called metabologenomics. This network uses an approach that emphasizes metabolite-gene consensus biochemical processes. Combining metabolomics and genomics data is beneficial. Furthermore, computer networking proposes that using metabolomics data may improve annotations in sequenced species and provide testable hypotheses for specific biochemical processes.

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References
1.
Ponsuksili S, Trakooljul N, Hadlich F, Methling K, Lalk M, Murani E . Genetic Regulation of Liver Metabolites and Transcripts Linking to Biochemical-Clinical Parameters. Front Genet. 2019; 10:348. PMC: 6478805. DOI: 10.3389/fgene.2019.00348. View

2.
Hsu C, Chen Y, Hsieh Y, Chang K, Hsueh P, Chen T . Integrated analyses utilizing metabolomics and transcriptomics reveal perturbation of the polyamine pathway in oral cavity squamous cell carcinoma. Anal Chim Acta. 2019; 1050:113-122. DOI: 10.1016/j.aca.2018.10.070. View

3.
Navarro-Munoz J, Selem-Mojica N, Mullowney M, Kautsar S, Tryon J, Parkinson E . A computational framework to explore large-scale biosynthetic diversity. Nat Chem Biol. 2019; 16(1):60-68. PMC: 6917865. DOI: 10.1038/s41589-019-0400-9. View

4.
van der Hooft J, Mohimani H, Bauermeister A, Dorrestein P, Duncan K, Medema M . Linking genomics and metabolomics to chart specialized metabolic diversity. Chem Soc Rev. 2020; 49(11):3297-3314. DOI: 10.1039/d0cs00162g. View

5.
Henneges C, Bullinger D, Fux R, Friese N, Seeger H, Neubauer H . Prediction of breast cancer by profiling of urinary RNA metabolites using Support Vector Machine-based feature selection. BMC Cancer. 2009; 9:104. PMC: 2680413. DOI: 10.1186/1471-2407-9-104. View